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BGPsec Deployment Challenges and Optimization Efforts

BGPsec Deployment Challenges and Optimization Efforts

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student conference on research and development (scored)

作者： M.E Abdelhafez Yahia Fadlalla School of Science and Engineering Malaysia University of Science and Technology (MUST) Selangor Malaysia InfoSec Consulting Lead Consultant/Researcher Hamilton Ontario Canada

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Border Gateway Protocol (BGP) is a standardized exterior gateway protocol by the InternetEngineering Task Force (IETF). As an inter-domain routing protocol, BGP facilitates the exchange of routing information among Autonomous Systems (ASes) to ensure the reachability of a network across the Internet. However, BGP was originally developed without security considerations, rendering it vulnerable to various attacks. Consequently, several security flaws have been discovered within the protocol. In response to security vulnerabilities in BGP, a number of techniques have been introduced such as: Internet Routing Registry (IRR), Resource Public Key Infrastructure (RPKI), and BGPsec. IRR serves as a platform for sharing routing policy information among network operators, enhancing coordination, and enabling the enforcement of desired routing behaviors. RPKI, on the other hand, provides a cryptographic model for verifying the association of network resources with the correct originating AS number, bolstering the authenticity and integrity of routing announcements. BGPsec facilitates AS path validation, consequently further enhances the security of BGP routing. This paper explores the challenges associated with the deployment of the BGPsec extension and drawing insights from state-of-the-art studies. This paper focuses on the BGPsec deployment computation and memory cost. By exploring and discussing the proposed optimization methods, this research sheds light on the current state of BGPsec deployment and provides valuable perspectives for future enhancements in securing the BGP protocol.

关键词： Quantum computing Costs Routing Routing protocols Robustness Border Gateway Protocol Internet Software defined networking research and development Resilience

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OSINT and AI: A Powerful Combination for Company Vulnerability Detection

OSINT and AI: A Powerful Combination for Company Vulnerabili...

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student conference on research and development (scored)

作者： Kenan Malween Bizouarn Mohamed Abdulnabi Joco Tan School of Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

Cybersecurity caused major concerns for organizations that protect critical data from cyber-attacks. Vulnerability scanning is a critical step as part of the proactive approach, as it allows the identification of potential vulnerabilities in the infrastructure before malicious attackers exploit them. This paper outlines the development of a tool for open-source intelligence (OSINT) designed to collect information from openly accessible sources efficiently. The collected information will then be formatted and sorted with clustering algorithms. Machine learning will be employed to detect potential vulnerabilities in the collected data. The vulnerability results can be presented in a report to the organizations to ease the concern. The aim is to increase awareness of how the OSINT could detect vulnerabilities and improve cybersecurity postures. This study aims to contribute to the existing knowledge base on vulnerability detection, OSINT applications in IT security, and combining new IT security detection solutions like machine learning.

关键词： Machine learning algorithms Knowledge based systems Clustering algorithms Machine learning Companies research and development Cyberattack

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L2 Regularization to Mitigate Overfitting in Email Phishing Attack Detection by CNN-LSTM

L2 Regularization to Mitigate Overfitting in Email Phishing ...

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student conference on research and development (scored)

作者： Aji Gautama Putrada Rizky Farras Aushaf School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Several studies have used convolutional neural networks and long short-term memory (CNN-LSTM) models to detect phishing emails because of their ability to capture attack patterns from raw data well. However, using CNN-LSTM to capture patterns in phishing email text datasets can cause overfitting problems. This research aims to evaluate the effectiveness of L2 Regularization in overfitting phishing email detection by CNN-LSTM. We obtained the phishing email detection dataset from Kaggle. We applied several natural language processing (NLP) pre-processing, such as text cleaning, tokenizing, padding, and Count Vectorization. We then applied CNN-LSTM with L2 Regularization and benchmarked it with four other methods: Original, Dropout, global vector for word embedding (GloVe Embedding), and Combined Method. We used validation loss curve, accuracy, sensitivity, specificity, and g-mean metrics to compare the performance of each method. The test results with Count Vectorization show that the target of the phishing email attack is at the university and that the characteristics of the attack are marked by polite language. This is seen as an attempt by the attacker to make the target more persuasive. The results of the validation loss curve show that the L2 Regularization model training does not experience overfitting, and its curve looks smooth, unlike other methods. Finally, L2 Regularization has the best g-mean compared to other methods, where the value is 0.926.

关键词： Training Sensitivity Accuracy Phishing Natural language processing Vectors Electronic mail Convolutional neural networks research and development Overfitting

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Potential Impacts of Wildfire on Distribution Networks in Thailand: A Data-Driven Assessment Using Hotspot Analysis

Potential Impacts of Wildfire on Distribution Networks in Th...

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student conference on research and development (scored)

作者： Pongpavee Phantanaikasem Akekachai Pannawan Chaowanan Jamroen Tossaporn Surinkaew Division of Instrumentation and Automation Engineering Technology Faculty of Engineering and Technology King Mongkut's University of Technology North Bangkok Rayong Campus Rayong Thailand School of International and Interdisciplinary Engineering Programs School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

In Thailand, the risk of wildfires has increased due to climate change, deforestation, and land-use practices. Distribution networks with feeders that traverse fire-prone forests and grasslands are particularly vulnerable to wildfire damage. Therefore, this study assesses the potential risks that wildfires pose to distribution networks in Thailand by analyzing wildfire hotspot data across different regions. Hotspot data, derived from satellite imagery, provides valuable insights into the frequency and intensity of wildfires in specific areas, which can be correlated with the location and layout of distribution networks. This study aims to identify areas where distribution networks could be at the greatest risk of wildfire-induced disruptions. The study focuses on four major regions of Thailand: the Northern, Northeastern, Central, and Southern regions. Each region has distinct geographical and climatic characteristics that influence wildfire patterns and their potential impacts on infrastructure. By analyzing regional variations in wildfire hotspots, this study aims to provide a preliminary assessment of wildfire risks to distribution networks and offer insights into potential mitigation strategies. The study results are valuable for policymakers, utility companies, and emergency management agencies as they seek to strengthen the resilience of Thailand’s electricity distribution system in the face of increasing wildfire threats.

关键词： Electric potential Wildfires Prevention and mitigation Layout Distribution networks Companies Market research Satellite images research and development Resilience

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Secure E-Voting System for student Associations in Private University Using Paillier Algorithm

Secure E-Voting System for Student Associations in Private U...

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student conference on research and development (scored)

作者： Ivan Tenusa Eunike Endariahna Surbakti Fenina Adline Twince Tobing Adhi Kusnadi Informatics Study Program Informatics and Engineering Faculty Universitas Multimedia Nusantara

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

The representatives of student associations at Private University are entrusted with the responsibility of empowering, facilitating, and nurturing the growth of the students, lecturers, and alumni within their respective programs. If the elected representatives do not align with the general consensus, problems are bound to arise. Therefore, student participation during the annual voting period is of utmost importance. When voting participation is low, the risk of the majority voice not being heard increases. Data from the General Election Commission in the private university reveals that among the four student associations in the Tech and IT Faculty, three of them have a participation rate lower than 50%.This research aims to develop an E-Voting system for student Associations within the university, with the intention of replacing the currently utilized Google Form system and enhancing student participation in future voting periods. While constructing an electronic voting system, the security and confidentiality of participants are vital. The Paillier homomorphic encryption algorithm is employed in the system to encrypt and decrypt voting messages sent by voters, ensuring their confidentiality. This algorithm was chosen due to its relative recency, speed, and relevance to this research in comparison to other algorithms. A user acceptance test conducted using the Technology Acceptance Model (TAM) has demonstrated that the application has been successfully developed, with user assessments of 87.50% for perceived ease of use, 72.27% for perceived usefulness, 74.74% for behavioral intention, and 86.46% for actual usage.

关键词： Technology acceptance model Electronic voting systems Internet Cryptography Electronic voting Homomorphic encryption research and development

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Analysis of Photovoltaic Cell Defect Classification Using Residual Neural Network

Analysis of Photovoltaic Cell Defect Classification Using Re...

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student conference on research and development (scored)

作者： Ainur Rafiq Assoli Shahrani Shahbudin Yuslinda Wati Mohamad Yusof Maizatul Zolkapli School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Selangor Malaysia Enabling Internet of Things Technologies Research Group School of Electrical Engineering College Of Engineering Universiti Teknologi MARA Shah Alam Selangor Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

The photovoltaic (PV) system has seen significant advancements in recent years. Accurate defect classification is essential for maximizing the energy output of PV cells throughout their lifespan. With the rapid progress in deep learning and convolutional neural networks (CNN), PV images can now be leveraged for defect classification, offering improvements over traditional methods. This work proposes an analysis of PV cell defect classification using two different types of Residual Neural Networks (ResNet), namely ResNet-18 and ResNet-50, to determine which architecture delivers the best performance. The results are further compared with other CNN architectures, including deep CNN, GoogLeNet, and VGG-16. Various performance metrics were evaluated and compared across these models. The findings reveal that ResNet-18 outperforms the other architectures, achieving the highest performance with an accuracy of $\mathbf{9 7. 9 6 \%}$, specificity of $\mathbf{9 9. 2 2 \%}$, sensitivity of $\mathbf{9 6. 5 9 \%}$, precision of $\mathbf{9 7. 4 2 \%}$, and an $\mathbf{F 1}$-score of $\mathbf{9 6. 8 0 \%}$. This analysis will help to improve the defects classification system for PV cells.

关键词： Photovoltaic systems Deep learning Accuracy Sensitivity Photovoltaic cells Neural networks Computer architecture Convolutional neural networks research and development Residual neural networks

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Medical Data Imputation by Hybrid Optimized Fuzzy C-Means and Ant Lion Optimization Algorithm

Medical Data Imputation by Hybrid Optimized Fuzzy C-Means an...

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student conference on research and development (scored)

作者： Mohamad Faiz Dzulkalnine Khyrina Airin Fariza Abu Samah Sulaiman Mahzan College of Computing Informatics and Mathematics Universiti Teknologi MARA Melaka Branch Jasin Campus Melaka Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

This paper uses a nature-inspired optimization algorithm called Ant Lion Optimization (ALO) to optimize the parameters of an imputation method, Fuzzy c-means (FCM). FCM is severely affected by the selection of parameters c and $\mathbf{m}$, which control the cluster number and fuzziness variable, respectively. Without the appropriate parameters selection, the prediction provided by FCM will result in low accuracy imputation. The traditional trial and error approach, which combines the parameters, is not reliable and prone to error as the value of c can range from 2 to the number of data while the value of m can range from 1 to infinity. Thus, a hybrid with a reliable optimization algorithm that can escape local optima and have fast computational time is appropriate. As such, a hybrid with an ALO optimization algorithm is proposed, inspired by the antlion hunting mechanism in nature. The proposed hybrid FCM-ALO was tested on three medical datasets: fertility, diabetes, and heart disease. Five missing rates are also used to test the proposed method’s reliability in increasing the rate of missing values further. The experimental results show that FCM-ALO produced a low error rate in Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) in all the datasets in all missing rates. Numerical and comparative results show that the proposed hybrid optimized imputation method has good convergence ability and better imputation quality in low computational time.

关键词： Heart Accuracy Imputation Diabetes Reliability Optimization Standards research and development Convergence Diseases

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G-Mean for Optimum Threshold in Anomaly Detection with Autoencoder: Cyber Security on The RT-IOT2022 Dataset

G-Mean for Optimum Threshold in Anomaly Detection with Autoe...

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student conference on research and development (scored)

作者： Aji Gautama Putrada Dyah Ayu Suci Ilhami School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Autoencoder is one of the most prominent methods in detecting attacks in RT-IoT2022 because the unsupervised learning method can capture abnormal patterns in the dataset. However, finding the optimum threshold is challenging in autoencoder-based anomaly detection, while false positives and false negatives are delicate matters in cybersecurity. This study aims to use g-mean to obtain the optimum threshold in autoencoder-based anomaly detection on the RT-IoT2022 dataset. We received the dataset from Kaggle and then applied several pre-processing methods, such as label encoder and normalization. The next step is to design and train the autoencoder model for anomaly detection. The receiver operating curve (ROC) and area under the curve (AUC) show how the autoencoder separates anomalous and non-anomalous data distribution. G-mean determines the optimum threshold for anomaly detection. We benchmark the g-mean with significance level alpha, z-score, and Bayesian model. The test results show that applying an autoencoder can increase the mean difference between normal and anomaly data from 0.0414 to 0.0453. Then, the autoencoder with g-mean thresholding has the highest accuracy, sensitivity, and g-mean compared to alpha, z-score, and Bayesian models, which are 0.94,0.94, and 0.93, respectively. Although its specificity is not the highest, its value is still above 0.9. Other methods cannot maintain accuracy, sensitivity, and g-mean above 0.4.

关键词： Sensitivity Accuracy Quantization (signal) Autoencoders Force Receivers Bayes methods Anomaly detection Unsupervised learning research and development

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Reconfigurable Multiband RF Energy Harvesting System in CMOS for Adaptive Power Efficiency

Reconfigurable Multiband RF Energy Harvesting System in CMOS...

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student conference on research and development (scored)

作者： Wen Xun Lian Alvin Kean Jun Xiang Harikrishnan Ramiah Kian Lun Soon Sooriamoorthy Denesh Nohaidda Sariff Naimah Mat Isa School of Engineering Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia School of Engineering Taylors University Malaysia Selangor Malaysia School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

This paper presents the design and implementation of a reconfigurable, fully integrated multiband Radio Frequency Energy Harvesting (RFEH) system in CMOS technology. The proposed architecture dynamically selects the frequency band with the highest ambient power density, thereby optimizing power conversion efficiency (PCE) for low-power applications such as the Internet of Things (IoT). While conventional RFEH systems rely on single-band designs or offchip multiband systems, they face significant limitations in adaptability and compactness, often resulting in reduced efficiency in real-world RF environments. Our design incorporates an on-chip tunable impedance matching network and a control circuit that manages frequency switching between 0.96 GHz and 1.8 GHz, targeting both compact integration and adaptability. Simulation results demonstrate that the system achieves a peak PCE of $\mathbf{3 8. 3 4 \%}$ across a dynamic range of 22 dB. The fully integrated architecture, occupying $900 \mu \mathrm{~m} \times$ $820 \mu \mathrm{~m}$, provides a viable solution for high-efficiency, small-form-factor energy harvesting systems.

关键词： Radio frequency Power system measurements Simulation Impedance matching System-on-chip Energy harvesting Internet of Things Power conversion research and development Switching circuits

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Transform Non-Touchable Screen into Touchable Screen Using Ultrasonic Sensor HC-SR04T

Transform Non-Touchable Screen into Touchable Screen Using U...

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student conference on research and development (scored)

作者： Usman Ali Waqas Ali Muhammad Umer Ramzan Muhammad Ehsan Rana Abdul Quim Dept. of Computer Science GIFT University Gujranwala Pakistan Dept. of Electrical Engineering (RCET) University of Engg. & Tech. Lahore Lahore Pakistan School of Computing Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

The paper presents a pioneering approach to revolutionizing non-touch screens into touch-enabled interfaces by leveraging ultrasonic sensor technology. The focal point of this research lies in the development and implementation of a system that seamlessly integrates ultrasonic sensors HC-SR04T with the Arduino Nano microcontroller. A comprehensive evaluation is being conducted through a series of seven diverse test operations executed across ten distinct screens. The results exhibited a remarkable outcome, with an average accuracy exceeding 95 percent. This noteworthy accomplishment underscores the robustness and efficacy of the developed system i n transforming non-touch screens into touch-sensitive interfaces.

关键词： Accuracy Microcontrollers Transforms Acoustics Robustness research and development

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